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At least 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Designing and Implementing a Distributed System Architecture for the Mars Rover Mission Planning Software (Maestro)

Distributed systems allow scientists from around the world to plan missions concurrently, while being updated on the revisions of their colleagues in real time. However, permitting multiple clients to simultaneously modify a single data repository can quickly lead to data corruption or inconsistent states between users. Since our message broker, the Java Message Service, does not ensure that messages will be received in the order they were published, we must implement our own numbering scheme to guarantee that changes to mission plans are performed in the correct sequence. Furthermore, distributed architectures must ensure that as new users connect to the system, they synchronize with the database without missing any messages or falling into an inconsistent state. Robust systems must also guarantee that all clients will remain synchronized with the database even in the case of multiple client failure, which can occur at any time due to lost network connections or a user's own system instability. The final design for the distributed system behind the Mars rover mission planning software fulfills all of these requirements and upon completion will be deployed to MER at the end of 2005 as well as Phoenix (2007) and MSL (2009).

Goldgof, Gregory M.↗
NASA NTRS2005

Dynamic Domains in Data Production Planning

This paper discusses a planner-based approach to automating data production tasks, such as producing fire forecasts from satellite imagery and weather station data. Since the set of available data products is large, dynamic and mostly unknown, planning techniques developed for closed worlds are unsuitable. We discuss a number of techniques we have developed to cope with data production domains, including a novel constraint propagation algorithm based on planning graphs and a constraint-based approach to interleaved planning, sensing and execution.

Golden, Keith↗
NASA NTRS2005

Addendum to Air Quality: Decision Support Tools, Partner Plans, Working Groups, Committees

In the original report dated February 11, 2005, the utility of the NASA Earth science data in the air quality activities of other agencies and organizations was assessed by reviewing strategic and mission plans and by conducting personal interviews with agency experts to identify and investigate agencies with the potential for partnership with NASA. The overarching agency strategic plans were reviewed and commonalities such as the desire for partnerships and technology development were noted. The addendum to the original report contains such information about the Tennessee Valley Authority and will be inserted in Section 2.6 of "Air Quality Decision Support Tools, Partner Plans, Working Groups, Committees".

Holekamp, Kara↗
NASA NTRS2005

Temporal and Resource Reasoning for Planning, Scheduling and Execution in Autonomous Agents

This viewgraph slide tutorial reviews methods for planning and scheduling events. The presentation reviews several methods and uses several examples of scheduling events for the successful and timely completion of the overall plan. Using constraint based models the presentation reviews planning with time, time representations in problem solving and resource reasoning.

Muscettola, Nicola↗
NASA NTRS1996

Static and Completion Analysis for Planning Knowledge Base Development and Verification

A key obstacle hampering fielding of AI planning applications is the considerable expense of developing, verifying, updating, and maintaining the planning knowledge base (KB). Planning systems must be able to compare favorably in terms of software lifecycle costs to other means of automation such as scripts or rule-based expert systems.

anaylsis techniques↗
NASA NTRS1997

Goal-driven Automation of a Deep Space Communications Station: A Case Study in Knowledge Engineering for Plan Generation and Execution

This paper describes the application of Artificial Intelligence techniques for plan generation, plan execution, and plan monitoring to automate a Deep Space Communication Station. This automation allows a Communication station to respond to a set of tracking goals by appropriately reconfiguring the communications hardware and software to provide the requested communications services.

Artificial Intelligence↗
NASA NTRS2004

Planning for rover opportunistic science

The Mars Exploration Rover Spirit recently set a record for the furthest distance traveled in a single sol on Mars. Future planetary exploration missions are expected to use even longer drives to position rovers in areas of high scientific interest. This increase provides the potential for a large rise in the number of new science collection opportunities as the rover traverses the Martian surface. In this paper, we describe the OASIS system, which provides autonomous capabilities for dynamically identifying and pursuing these science opportunities during longrange traverses. OASIS uses machine learning and planning and scheduling techniques to address this goal. Machine learning techniques are applied to analyze data as it is collected and quickly determine new science gods and priorities on these goals. Planning and scheduling techniques are used to alter the behavior of the rover so that new science measurements can be performed while still obeying resource and other mission constraints. We will introduce OASIS and describe how planning and scheduling algorithms support opportunistic science.

artificial intelligence↗
NASA NTRS2003

On-board autonomous attitude maneuver planning for planetary spacecraft using genetic algorithms

A key enabling technology that leads to greater spacecraft autonomy is the capability to autonomously and optimally slew the spacecraft from and to different attitudes while operating under a number of celestial and dynamic constraints. The task of finding an attitude trajectory that meets all the constraints is a formidable one, in particular for orbiting or fly-by spacecraft where the constraints and initial and final conditions are of time-varying nature. This paper presents an approach for attitude path planning that makes full use of a priori constraint knowledge and is computationally tractable enough to be executed on-board a spacecraft. The approach is based on incorporating the constraints into a cost function and using a Genetic Algorithm to iteratively search for and optimize the solution. This results in a directed random search that explores a large part of the solution space while maintaining the knowledge of good solutions from iteration to iteration. A solution obtained this way may be used 'as is' or as an initial solution to initialize additional deterministic optimization algorithms. A number of example simulations are presented including the case examples of a generic Europa Orbiter spacecraft in cruise as well as in orbit around Europa. The search times are typically on the order of minutes, thus demonstrating the viability of the presented approach. The results are applicable to all future deep space missions where greater spacecraft autonomy is required. In addition, onboard autonomous attitude planning greatly facilitates navigation and science observation planning, benefiting thus all missions to planet Earth as well.

genetic algorithm↗
NASA NTRS2003

Create your own science planning tool in 3 days with SOA

Scientific discovery and advancement of knowledge has been, and continues to be, the goal for space missions at Jet Propulsion Laboratory. Scientist must plan their observation/experiments to get the maximum data return in order to make those discoveries. However, each mission has different science objectives, a different spacecraft and different instrument payloads, as well as, different routes to different destinations with different spacecraft restrictions and characteristics. In the current reduced cost environment, manageable cost for mission planning software is a must. Science Opportunity Analyzer (SOA), a planning tool for scientists and mission planners, utilizes a simple approach to reduce cost and promote reusability.

Science Opportunity Analyzer (SOA).↗
NASA NTRS2006

Model-Unified Planning and Execution for Distributed Autonomous System Control

The Intelligent Distributed Execution Architecture (IDEA) is a real-time architecture that exploits artificial intelligence planning as the core reasoning engine for interacting autonomous agents. Rather than enforcing separate deliberation and execution layers, IDEA unifies them under a single planning technology. Deliberative and reactive planners reason about and act according to a single representation of the past, present and future domain state. The domain state behaves the rules dictated by a declarative model of the subsystem to be controlled, internal processes of the IDEA controller, and interactions with other agents. We present IDEA concepts - modeling, the IDEA core architecture, the unification of deliberation and reaction under planning - and illustrate its use in a simple example. Finally, we present several real-world applications of IDEA, and compare IDEA to other high-level control approaches.

Aschwanden, Pascal↗
NASA NTRS2004

Execution of the Spitzer In-orbit Checkout and Science Verification Plan

The Spitzer Space Telescope is an 85-cm telescope with three cryogenically cooled instruments. Following launch, the observatory was initialized and commissioned for science operations during the in-orbit checkout (IOC) and science verification (SV) phases, carried out over a total of 98.3 days. The execution of the IOC/SV mission plan progressively established Spitzer capabilities taking into consideration thermal, cryogenic, optical, pointing, communications, and operational designs and constraints. The plan was carried out with high efficiency, making effective use of cryogen-limited flight time. One key component to the success of the plan was the pre-launch allocation of schedule reserve in the timeline of IOC/SV activities, and how it was used in flight both to cover activity redesign and growth due to continually improving spacecraft and instrument knowledge, and to recover from anomalies. This paper describes the adaptive system design and evolution, implementation, and lessons learned from IOC/SV operations. It is hoped that this information will provide guidance to future missions with similar engineering challenges

operations↗
NASA NTRS2007

EVA Task Timing and Timeline Planning

EVA timeline development occurs using task execution data generated through underwater training and simulation. This project collected task time data during final training events for several Space Shuttle and International Space Station missions and compared like task time data collected during on-orbit execution. Analysis was performed to compare types of activities and times required for each looking specifically for how activities can be accurately trained from a timeline planning perspective. The data revealed two significant aspects of flight timeline planning; Zero-g task times will match training times for activities that can be accurately simulated with appropriate fidelity hardware; and not all activities can be simulated sufficiently to produce training task times that will reflect required zero-g times. An approach for timeline planning utilizing this knowledge is also presented.

Looper, Christopher A.↗
NASA NTRS2007

Implementation of a Collision Probability Prediction Technique for Constellation Maneuver Planning

On March 22, 2006, the Space Technology 5 (ST5) constellation spacecraft were successfully delivered to orbit by a Pegasus XI, launch vehicle. An unexpected relative motion experienced by the constellation after orbit insertion brought about a problem. Soon after launch the observed relative position of the inert rocket body was between the leading and the middle spacecraft within the constellation. The successful planning and execution of an orbit maneuver that would create a fly-by of the rocket body was required to establish the.formation. This maneuver would create a close approach that needed to conform to predefined collision probability requirements. On April 21, 2006, the ST5 "155" spacecraft performed a large orbit maneuver and successfully passed the inert Pegasus 3rd Stage Rocket Body on April 30, 2006 15:20 UTC at a distance of 2.55 km with a Probability of Collision of less than 1.0E-06. This paper will outline the technique that was implemented to establish the safe planning and execution of the fly-by maneuver. The method makes use of Gaussian distribution models of state covariance to determine underlying probabilities of collision that arise under low velocity encounters. Specific numerical examples used for this analysis are discussed in detail. The mechanics of this technique are explained to foster deeper understanding of the concepts presented and to improve existing processes for use in future constellation maneuver planning.

Concha, Marco a.↗
NASA NTRS2007

The ANMLite Language and Logic for Specifying Planning Problems

We present the basic concepts of the ANMLite planning language. We discuss various aspects of specifying a plan in terms of constraints and checking the existence of a solution with the help of a model checker. The constructs of the ANMLite language have been kept as simple as possible in order to reduce complexity and simplify the verification problem. We illustrate the language with a specification of the space shuttle crew activity model that was constructed under the Spacecraft Autonomy for Vehicles and Habitats (SAVH) project. The main purpose of this study was to explore the implications of choosing a robust logic behind the specification of constraints, rather than simply proposing a new planning language.

Butler, Ricky W.↗
NASA NTRS2005

Method and system for entering data within a flight plan entry field

The present invention provides systems, apparatus and methods for entering data into a flight plan entry field which facilitates the display and editing of aircraft flight-plan data. In one embodiment, the present invention provides a method for entering multiple waypoint and procedure identifiers at once within a single a flight plan entry field. In another embodiment, the present invention provides for the partial entry of any waypoint or procedure identifiers, and thereafter relating the identifiers with an aircraft's flight management system to anticipate the complete text entry for display. In yet another embodiment, the present invention discloses a method to automatically provide the aircraft operator with selectable prioritized arrival and approach routing identifiers by a single manual selection. In another embodiment, the present invention is a method for providing the aircraft operator with selectable alternate patterns to a new runway.

Gibbs, Michael J.↗
NASA NTRS2008

Planning for the Future, a Look from Apollo to the Present

Future missions out of low Earth orbit, returning to the moon and Mars, will be some of the most complicated endeavors ever attempted by mankind. It will require the wealth of nations and the dedicated efforts of thousand of individuals working in a concerted effort to take man to the moon, Mars and beyond. These missions will require new equipment and new approaches to optimize our limited resources and time in space. This daily planning and optimization which currently is being performed by scores of people in MCC Houston and around the world will need to adapt to the challenges faced far from Earth. By studying the processes, methodologies, and tools employed from Apollo, Skylab, Shuttle, ISS, and other programs such as NEEMO, we can learn from the past to plan for the future. This paper will explore the planning process used from Apollo onward and will discuss their relevancy in future applications.

Segrera, David↗
NASA NTRS2006

Notional Airspace Operations Demonstration Plan

The airspace operations demonstration (AOD) is intended to show that the Access 5 Step 1 functional requirements can be met. The demonstration will occur in two phases. The initial on-range phase will be carried out in restricted airspace to demonstrate the cooperative collision avoidance (CCA) functional requirements and to provide risk-reduction for the AOD by allowing the test team to rehearse some elements of the demonstration mission. The CCA system to be used in these flights is based on Automatic Dependent Surveillance-Broadcast (ADS-B) which is a commercially-available system by which airplanes constantly broadcast their current position and altitude to other aircraft and ground resources over a dedicated radio datalink. The final phase will occur in the national airspace (NAS) and will be the formal demonstration of the remainder of the proposed functional requirements. The general objectives of the AOD are as follows: (1) Demonstrate that the UAS can aviate in the NAS (2) Demonstrate that the UAS can navigate in the NAS (3) Demonstrate that the UAS can communicate with the NAS (4) Demonstrate that the UAS can perform selected collision avoidance functions in the NAS (5) Demonstrate that the UAS can evaluate and avoid weather conflicts in the NAS (6) Demonstrate that the UAS can provide adequate command and control in the NAS In addition to the stated objectives, there are a number of goals for the flight demonstration. The demo can be accomplished successfully without achieving these goals, but these goals are to be used as a guideline for preparing for the mission. The goals are: (1) Mission duration of at least 24 hours (2) Loiter over heavy traffic to evaluate the data block issue identified during the Access 5 Airspace Operations Simulations (3) Document the contingency management process and lessons learned (4) Document the coordination process for Ground Control Stations (GCS) handoff (5) Document lessons learned regarding the process of flying in the NAS Preliminary planning for a notional mission to achieve the objectives and goals has been prepared. The planning is intended to serve as a guide for detailed planning of the AOD.

Trongale, Nicholas A.↗